Biometric authentication is researched due to the need for securing smartphones. As the number of smartphones in society increases, so do the possibilities that we have with our devices. These possibilities increase the potential damage that may be done by an attacker. Passive authentication methods could increase security without having a negative impact on the user experience. In this thesis, we propose an LSTM-based model that can passively authenticate smartphone users based on accelerometer data. We train and test our model on windows with a length of three seconds. This is far below the window lengths used in comparable studies such as the HMOG study [21] and in DeepAuth [7]. The proposed method achieves an AUC of 82\% and an EER of 24.1\% for the HMOG dataset [21]. Furthermore, we experiment with how different window lengths affect the accuracy of our model. The best results are achieved when using a length of three seconds. We also explore the performance of the model when trained on a small subset of the data. The experiment is performed to evaluate our model in a scenario that we consider to be more realistic. Our results for the experiment is an AUC of 75.84\% and an EER of 29.28\%. We also present insights gained from reproducing results from the scientific literature. We also present insights gained from reproducing results from the scientific literature.